Patch-based models and algorithms for image denoising: a comparative review between patch-based images denoising methods for additive noise reduction
Alkinani and El-Sakka EURASIP Journal on Image and Video
Processing
Patch-based models and algorithms for image denoising: a comparative review between patch-based images denoising methods for additive noise reduction
Monagi H. Alkinani 1
Mahmoud R. El-Sakka 0
0 Department of Computer Science, Middlesex College, Western University , 1151 Richmond Street, N6A 5B7, London, Ontario , Canada
1 Department of Computer Science, University of Jeddah , Asfan Road, 285, Dhahban 23881, Jeddah , Saudi Arabia
Background: Digital images are captured using sensors during the data acquisition phase, where they are often contaminated by noise (an undesired random signal). Such noise can also be produced during transmission or by poor-quality lossy image compression. Reducing the noise and enhancing the images are considered the central process to all other digital image processing tasks. The improvement in the performance of image denoising methods would contribute greatly on the results of other image processing techniques. Patch-based denoising methods recently have merged as the state-of-the-art denoising approaches for various additive noise levels. In this work, the use of the state-of-the-art patch-based denoising methods for additive noise reduction is investigated. Various types of image datasets are addressed to conduct this study. Methods: We first explain the type of noise in digital images and discuss various image denoising approaches, with a focus on patch-based denoising methods. Then, we experimentally evaluate both quantitatively and qualitatively the patch-based denoising methods. The patch-based image denoising methods are analyzed in terms of quality and computational time. Results: Despite the sophistication of patch-based image denoising approaches, most patch-based image denoising methods outperform the rest. Fast patch similarity measurements produce fast patch-based image denoising methods. Conclusion: Patch-based image denoising approaches can effectively reduce noise and enhance images. Patch-based image denoising approach is the state-of-the-art image denoising approach.
Patch-based image denoising; Bilateral filter; Non-local means filtering; Probabilistic patch-based filtering; Dictionary learning filtering; K-SVD; Gaussian patch-PCA filtering; BM3D
1 Review
1.1 Introduction
The noise level in digital images may vary from being
almost imperceptible to being very noticeable. Image
denoising techniques attempt to produce a new image that
has less noise, i.e., closer to the original noise-free image.
Image denoising techniques can be grouped into two main
approaches: pixel-based image filtering and patch-based
image filtering. A pixel-based image filtering scheme is
mainly a proximity operation used for manipulating one
pixel at a time (pixel-wise) based on its spatial
neighboring pixels located within a kernel. On the other hand, in
patch-based image filtering, the noisy image is divided
into patches, or “blocks,” which are then manipulated
separately in order to provide an estimate of the true
pixel values (patch-wise) based on similar patches located
within a search window. This approach utilizes the
redundancy and the similarity among the various parts of the
input image. Figure 1 shows the mechanism of the two
approaches.
It is now common in image denoising field to
utilize patch-based models and algorithms instead of
pixelbased approaches to produce most promising estimate
of the noise-free images. However, there are both
advantages and disadvantages in the use of patch-based models
and algorithms. There are several advantages of
patchbased approaches. Smoothing flat regions is the most
important aspect. Redundancy between patches enable
patch-based approaches to properly smooth flat reigns. A
second advantage of using patch-based models and
algorithms approaches is that it can preserve fine image details
and sharp edges. However, there could be some
disadvantages for patch-based models and algorithms. First,
although similarity between patches assists in estimating
flat regions, so is the averaging. It is, therefore, quite
timeconsuming to group and compare similar patches. This
might mean that each patch has multiple estimates and
patches are overlapped. Secondly, while it may be that
patterns and textures seem clear with less noise,
patchbased models and algorithms usually exploit large number
of parameters, which can be extremely difficult to adjust
properly. We believe that the advantages of patch-based
methods far outweigh their disadvantages, as modern
computers are significantly fast, and have large memory
spaces.
In this work, the patch-based image denoising schemes
are analyzed from two different aspects: (1) the
performance of patch-based denoising techniques in terms of
image denoising quality and (2) the performance of
patchbased denoising techniques in terms of computational
time, where various patch-based denoising techniques are
addressed.
A literature survey was condu (...truncated)